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Key-Part Attention Retrieval for Robotic Object Recognition 期刊论文
TSINGHUA SCIENCE AND TECHNOLOGY, 2024, 卷号: 29, 期号: 3, 页码: 644-655
作者:  Liu, Jierui;  Cao, Zhiqiang;  Tang, Yingbo
Adobe PDF(2164Kb)  |  收藏  |  浏览/下载：105/14  |  提交时间：2024/02/22
Training  Visualization  Image recognition  Cameras  Object recognition  Convolutional neural networks  Data mining  key-part attention  retrieval  robotic object recognition  
Cross-Architecture Knowledge Distillation 会议论文
INTERNATIONAL JOURNAL OF COMPUTER VISION, Macau SAR, China, 2022.12.4-2022.12.8
作者:  Yufan Liu;  Jiajiong Cao;  Bing Li;  Weiming Hu;  Jingting Ding;  Liang Li
Adobe PDF(1020Kb)  |  收藏  |  浏览/下载：165/48  |  提交时间：2023/04/23
Knowledge distillation  Cross architecture  Model compression  Deep learning  
PDNet: Toward Better One-Stage Object Detection With Prediction Decoupling 期刊论文
IEEE Transactions on Image Processing, 2022, 卷号: 31, 页码: 5121-5133
作者:  Yang, Li;  Xu, Yan;  Wang, Shaoru;  Yuan, Chunfeng;  Zhang, Ziqi;  Li, Bing;  Hu, Weiming
Adobe PDF(3190Kb)  |  收藏  |  浏览/下载：319/41  |  提交时间：2022/09/19
Object detection  prediction decoupling  convolutional neural network  
Visual Question Answering With Dense Inter- and Intra-Modality Interactions 期刊论文
IEEE TRANSACTIONS ON MULTIMEDIA, 2021, 卷号: 23, 页码: 3518-3529
作者:  Liu, Fei;  Liu, Jing;  Fang, Zhiwei;  Hong, Richang;  Lu, Hanqing
Adobe PDF(2891Kb)  |  收藏  |  浏览/下载：331/73  |  提交时间：2021/12/28
Visualization  Knowledge discovery  Connectors  Encoding  Task analysis  Image coding  Stacking  Visual question answering  attention  dense interactions  
Progressively Refined Face Detection Through Semantics-Enriched Representation Learning 期刊论文
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, 2020, 卷号: 15, 期号: 1, 页码: 1394-1406
作者:  Li, Zhihang;  Tang, Xu;  Wu, Xiang;  Liu, Jingtuo;  He, Ran
Adobe PDF(6813Kb)  |  收藏  |  浏览/下载：265/42  |  提交时间：2021/03/15
Face detection  object detection  
Automated Silicon-Substrate Ultra-Microtome for Automating the Collection of Brain Sections in Array Tomography 期刊论文
IEEE-CAA JOURNAL OF AUTOMATICA SINICA, 2021, 卷号: 8, 期号: 2, 页码: 389-401
作者:  Cheng, Long;  Liu, Weizhou;  Zhou, Chao;  Zou, Yongxiang;  Hou, Zeng-Guang
Adobe PDF(17687Kb)  |  收藏  |  浏览/下载：304/51  |  提交时间：2021/03/08
Array tomography  automatic collection system  brain sections  microscopic object detection  serial section  
AutoDet: Pyramid Network Architecture Search for Object Detection 期刊论文
INTERNATIONAL JOURNAL OF COMPUTER VISION, 2021, 期号: 4, 页码: 1087-1105
作者:  Li, Zhihang;  Xi, Teng;  Zhang, Gang;  Liu, Jingtuo;  He, Ran
Adobe PDF(2604Kb)  |  收藏  |  浏览/下载：294/49  |  提交时间：2021/03/01
Object detection  Neural architecture search  Feature pyramids  
Show, Tell, and Polish: Ruminant Decoding for Image Captioning 期刊论文
IEEE TRANSACTIONS ON MULTIMEDIA, 2020, 卷号: 22, 期号: 8, 页码: 2149-2162
作者:  Guo, Longteng;  Liu, Jing;  Lu, Shichen;  Lu, Hanqing
Adobe PDF(4378Kb)  |  收藏  |  浏览/下载：232/36  |  提交时间：2020/08/31
Image captioning  Multi-pass decoding  Rumination  
Deep learning radiomic nomogram can predict the number of lymph node metastasis in locally advanced gastric cancer: an international multicenter study 期刊论文
ANNALS OF ONCOLOGY, 2020, 卷号: 31, 期号: 7, 页码: 912-920
作者:  Dong, Di;  Fang, Mengjie;  Tang, Lei;  Shan, Xiuhong;  Gao, Jianbo;  Giganti, Francesco;  Wang, Rongpin;  Chen, Xin;  Wang, Xiaoxiao;  Palumbo, Diego;  Fu, Jia;  Li, Wuchao;  Li, Jing;  Zhong, Lianzhen;  De Cobelli, Francesco;  Ji, Jiafu;  Liu, Zaiyi;  Tian, Jie
浏览  |  Adobe PDF(2209Kb)  |  收藏  |  浏览/下载：441/71  |  提交时间：2020/07/20
deep learning  locally advanced gastric cancer  lymph node metastasis  radiomic nomogram  
Deep Self-Evolution Clustering 期刊论文
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2020, 卷号: 42, 期号: 4, 页码: 809-823
作者:  Chang, Jianlong;  Meng, Gaofeng;  Wang, Lingfeng;  Xiang, Shiming;  Pan, Chunhong
浏览  |  Adobe PDF(4817Kb)  |  收藏  |  浏览/下载：439/95  |  提交时间：2020/06/02
Task analysis  Unsupervised learning  Training  Clustering methods  Pattern analysis  Clustering  deep self-evolution clustering  self-evolution clustering training  deep unsupervised learning